Mobile Health Applications for Secondary Prevention After Myocardial Infarction or PCI: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

移动健康应用程序在心肌梗死或经皮冠状动脉介入治疗后二级预防中的应用:随机对照试验的系统评价和荟萃分析

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Abstract

Background: Mobile health applications have emerged as a novel tool to support secondary prevention after myocardial infarction (MI) or percutaneous coronary intervention (PCI). However, the impact of app-based interventions on clinically meaningful outcomes such as hospital readmissions remains uncertain. Objective: To systematically evaluate the effectiveness of smartphone app-based interventions in reducing unplanned hospital readmissions among post-MI/PCI patients. Methods: A systematic search of PubMed was conducted for randomized controlled trials published between January 2020 and April 2025. Eligible studies evaluated smartphone apps designed for secondary cardiovascular prevention and reported on unplanned hospital readmissions. Risk ratios (RRs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Subgroup analyses were performed based on follow-up duration and user adherence. Results: Four trials encompassing 827 patients met inclusion criteria. App-based interventions were associated with a significant reduction in unplanned hospital readmissions compared to standard care (RR 0.45; 95% CI: 0.23-0.89; p = 0.0219). Greater benefits were observed in studies with longer follow-up durations and higher adherence rates. Improvements in patient-reported outcomes, including health-related quality of life, were also documented. Heterogeneity was moderate. Major adverse cardiovascular events (MACEs) were reported in only two studies and were not analyzed due to inconsistent definitions and low event rates. Conclusions: Smartphone applications for post-MI/PCI care are associated with reduced unplanned hospital readmissions and improved patient-reported outcomes. These tools may play a meaningful role in future cardiovascular care models, especially when sustained engagement and personalized features are prioritized.

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